Bayesian modelling and ROC analysis to predict placebo responders using clinical score measured in the initial weeks of treatment in depression trials

Placebo Responses and Their Clinical Implications in Fibromyalgia: A Meta-Analysis Using SSRI and SNRI Trials

Background: Fibromyalgia (FM) is a chronic primary pain condition, associated with widespread musculoskeletal pain, disturbed sleep, fatigue, cognitive dysfunction, and a range of comorbid conditions such as irritable bowel syndrome, and depression. Despite its high prevalence of 2% in the general population, FM continues to pose scientific and clinical challenges in definition, etiology, and day-to-day management. In terms of treatment, FM can be treated with selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs). Objective: Patients with FM and other chronic primary pain syndromes are known to experience substantial and clinically relevant placebo effects. An update of the placebo responses for various outcomes in the FM population and especially a discussion about clinical implications is therefore needed. Methods: We used data from a large data pool that includes randomized controlled trials (RCTs) examining within-placebo mean change scores of baseline vs. follow-up assessments in FM trials of SSRIs and SNRIs. The primary outcomes were pain, functional disability, and depression and using different scales. We assessed heterogeneity of included trials. Results: A total of 29 RCTs with N = 8,453 patients suffering from FM were included in our analysis. Within-placebo mean change scores of baseline vs. follow-up assessments were large for pain (mean change = 2.31, 95% CI: 0.42-4.21, p = 0.017), functional disability (mean change = 3.31, 95% CI: 2.37-4.26, p < 0.000), and depression (mean change = 1.55, 95% CI: 0.92-2.18, p < 0.000). Heterogeneity was found to be large for all outcomes. Impact: Our results provide preliminary evidence that placebo responses, which also consist of non-specific effects, might play a role in the treatment of FM. Furthermore, we highlight limitations of our analyses and make suggestions for future studies.

Two Trajectories of Depressive Symptom Reduction Throughout Behavioral Activation Teletherapy Among Underserved, Ethnically Diverse, Primary Care Patients: A VitalSign6 Report

While prior research has investigated trajectories of depressive symptom change throughout psychotherapy, such work has not been conducted exclusively among underserved patients receiving brief Behavioral Activation (BA) teletherapy, intervention modifications that should reduce barriers to therapy initiation and engagement. The current project used cluster analysis to determine discrete groups of symptom change among patients receiving an 8-session BA teletherapy intervention, and analyzed whether demographic and clinical characteristics were associated with group membership. Data from 105 patients referred from charity primary care clinics and receiving at least two therapy sessions were analyzed. Patients were predominantly female and Latina. The 9-item Patient Health Questionnaire (PHQ-9) was the outcome. Two categories were determined: a larger group (N = 61) demonstrating initially less severe symptoms and experiencing a gradual recovery, and a smaller group beginning with more severe symptoms, and experiencing a steeper recovery. In both groups, a majority of participants experienced at least a 5-point drop in depressive symptoms, while in the latter group, a majority of patients achieved depressive symptom remission (PHQ-9 < 5). Monolingual Spanish speakers were more likely to be in the former group, but no other demographic or clinical characteristics were associated with group membership. In both groups, a majority of the symptom reduction occurred by sessions 4-6. Therefore, two categories of depressive symptom change, slow responders and rapid responders, occur among patients receiving a brief BA teletherapy intervention. No demographic differences aside from primary language, nor any clinical characteristics, distinguish group membership, suggesting similar patterns of symptom reduction among a primarily underserved sample.

Application of Item Response Theory to Model Disease Progression and Agomelatine Effect in Patients with Major Depressive Disorder

INTRODUCTION

In this paper, we studied the effect over time of agomelatine, an antidepressant drug administered in patient with major depressive disorder, through item response theory (IRT), taking into account a strong placebo effect and missing not at random. We also assessed the informativeness of the HAMD-17 scale's item.

MATERIALS AND METHODS

The data includes five phase III clinical trials sponsored by Servier Institute, totalling 1549 patients followed during a maximum of 1 year. At each observation, individual scores for the 17 items of the HAMD scale were recorded. The probability for each score was modelled with IRT. A non-linear mixed effects model was used to describe the evolution of the disease and was coupled with a time to event model to predict dropout. Clinical trial simulations were then used to compare placebo and active treatment. Informativeness of each item was evaluated using the Fisher information theory.

RESULTS

The best model combined an IRT model, a longitudinal model for underlying depression which describes the remission and then a possible relapse, and a hazard model for dropout depending on the evolution from baseline. The drug effect was best modelled as an effect on the remission and the relapse phases. The median predicted drop in HAMD between baseline and 6 weeks was 8.8 (90% PI, 8.3-9.2) when on placebo and 13.1 (90% PI, 12.8-13.4) when treated. Nine items were found to be the most informative.

CONCLUSION

The IRT framework allowed to characterise the evolution of depression with time and estimate the effect of agomelatine, as well as the link between symptoms and disease.

Model-Informed Approach to Assess the Treatment Effect Conditional to the Level of Placebo Response

One of the most important reasons for failure of placebo-controlled randomized controlled clinical trials (RCTs) is the lack of appropriate methodologies for detecting treatment effect (TE; difference between placebo and active treatment response) in the presence of excessively low/high levels of placebo response. Although, the higher the level of placebo response in a trial, the lower the apparent detectable TE. TE is usually estimated in a conventional analysis of an RCT as an "apparent" TE value conditional to the level of placebo response in that RCT. A model-informed methodology is proposed to establish a relationship between level of placebo response and TE. This relationship is used to estimate the "typical" TE associated with a "typical" level of placebo response, irrespective of the level of placebo response observed. The approach can be valuable for providing a reliable estimate of TE, for conducting risk/benefit analysis, and for determining dosage recommendations.

An Innovative Disease-Drug-Trial Framework to Guide Binge Eating Disorder Drug Development: A Case Study for Topiramate

As with other psychiatric disorders, development of drugs to treat binge-eating disorder (BED) has been hampered by high placebo response and dropout rates in randomized controlled trials (RCTs). Although not approved for use in BED, several RCTs have suggested that topiramate is efficacious for BED in obese individuals. Using data from a positive investigator-initiated RCT of topiramate in 61 obese individuals with BED, the objective of the present study is (i) to develop a quantitative disease-drug-trial framework to inform future BED clinical trial designs, and (ii) to determine the optimal topiramate dose to achieve therapeutic efficacy. Disease-drug-trial models were developed separately for the two efficacy measures, namely, longitudinal normalized weekly binge-eating episode frequency (BEF) and binge day frequency (BDF). Model building consisted of (i) developing a placebo effect model that describes response from the placebo group, (ii) adding a drug effect to the placebo model to describe dose-response relationships, and (iii) developing a parametric time to event model to characterize patient dropout patterns. The placebo effect on normalized BEF and BDF over time demonstrated a maximum decrease of ~ 57% by 5 weeks. Participants had a higher dropout probability if no weight loss occurred during the trial period. The identified dose-response relationship demonstrated a daily dose of 125 mg was needed to exhibit a marked reduction in weekly BEF. The developed comprehensive disease-drug-trial model will be utilized to simulate different clinical trial designs to increase the success for future BED drug development programs.

Is It Time for Going Beyond the P-Value Paradigm With the Estimation of the Probability of Clinical Benefit as a Criterion for Assessing the Outcomes of a Clinical Trial? A Case Study in Patients With Major Depressive Disorder

The conventional statistical methodologies for evaluating treatment effect are based on hypothesis testing (P-value). Alternative measurements of treatment effect have been proposed for anti-infective treatments using the probability of target attainment. A general framework is proposed to extend this methodology to other therapeutic areas. A disease trial model is used for estimating the probability of reaching a treatment effect associated with relevant clinical benefits, in complement to the evaluation of the probability of rejecting the null hypothesis. A case study is presented in depression, where disease status is evaluated using bounded clinical scores (Hamilton Depression Rating Scale), and detectable treatment effect is inversely proportional to placebo response. The β-regression approach is used to model Hamilton scale scores, and a placebo-related criterion is proposed for determining the clinical benefit. The probability of reaching a clinical benefit represents a reliable criterion for replacing the P-value paradigm in the assessment of the outcomes of clinical trials.

Screening of cytochrome P450 3A4 inhibitors via in silico and in vitro approaches

Cytochrome P450 3A4 (CYP3A4) is an important member of the CYP family and responsible for metabolizing a broad range of drugs. It is necessary to establish virtual screening models for predicting CYP3A4 inhibitors.

Methods for assessing and controlling placebo effects

The placebo serves as an indispensable control in many randomized trials. When analyzing the benefit of a new treatment, researchers are often confronted with large placebo effects that diminish the treatment effect. Various alternative methods have been proposed for analyzing placebo and treatment effects in studies where large placebo effects are expected or have already occurred. This paper presents an overview of methodological work that has been proposed for assessing and/or controlling for placebo effects in randomized trials. Throughout this paper, two main approaches are discussed. The first approach considers designs that represent alternatives to the classical placebo-controlled randomized trial design. Separately, the second approach considers adopting new methods for the statistical analysis of placebo and treatment effects to be implemented after the data have been collected using a classical randomized trial design.

Disease Progression Modeling: Key Concepts and Recent Developments

Disease modeling involves the use of mathematical functions to describe quantitatively the time course of disease progression. In order to characterize the natural progression of disease, these models generally incorporate longitudinal data for some biomarker(s) of disease severity or can incorporate more direct measures of disease severity. Disease models are also often linked to pharmacokinetic-pharmacodynamic models so that the influence of drug treatment on disease progression can be quantified and evaluated. Regulatory agencies have embraced disease progression models as powerful tools that can be used to improve drug development productivity. This article provides a brief overview of key concepts in disease progression modeling followed by illustrative examples from models for Alzheimer's disease. Finally, recent novel applications in which disease progression models have been linked to cost-effectiveness analysis and genomic analysis are described.

Mechanisms of the placebo effect in pain and psychiatric disorders

Placebo effect research over the past 15 years has improved our understanding of how placebo treatments reduce patient symptoms. The expectation of symptom improvement is the primary factor underlying the placebo effect. Such expectations are shaped by past experiences, contextual cues and biological traits, which ultimately modulate one's degree of response to a placebo. The body of evidence that describes the physiology of the placebo effect has been derived from mechanistic studies primarily restricted to the setting of pain. Imaging findings support the role of endogenous opioid and dopaminergic networks in placebo analgesia in both healthy patients as well as patients with painful medical conditions. In patients with psychiatric illnesses such as anxiety disorders or depression, a vast overlap in neurological changes is observed in drug responders and placebo responders supporting the role of serotonergic networks in placebo response. Molecular techniques have been relatively underutilized in understanding the placebo effect until recently. We present an overview of the placebo responder phenotypes and genetic markers that have been associated with the placebo effect in pain, schizophrenia, anxiety disorders and depression.

A Novel Methodology to Estimate the Treatment Effect in Presence of Highly Variable Placebo Response

One of the main reasons for the inefficiency of multicenter randomized clinical trials (RCTs) in depression is the excessively high level of placebo response. The aim of this work was to propose a novel methodology to analyze RCTs based on the assumption that centers with high placebo response are less informative than the other centers for estimating the 'true' treatment effect (TE). A linear mixed-effect modeling approach for repeated measures (MMRM) was used as a reference approach. The new method for estimating TE was based on a nonlinear longitudinal modeling of clinical scores (NLMMRM). NLMMRM estimates TE by associating a weighting factor to the data collected in each center. The weight was defined by the posterior probability of detecting a clinically relevant difference between active treatment and placebo at that center. Data from five RCTs in depression were used to compare the performance of MMRM with NLMMRM. The results of the analyses showed an average improvement of ~15% in the TE estimated with NLMMRM when the center effect was included in the analyses. Opposite results were observed with MMRM: TE estimate was reduced by ~4% when the center effect was considered as covariate in the analysis. The novel NLMMRM approach provides a tool for controlling the confounding effect of high placebo response, to increase signal detection and to provide a more reliable estimate of the 'true' TE by controlling false negative results associated with excessively high placebo response.

Clinical pharmacology = disease progression + drug action

Clinical pharmacology is concerned with understanding how to use medicines to treat disease. Pharmacokinetics and pharmacodynamics have provided powerful methodologies for describing the time course of concentration and effect in individuals and in populations. This population approach may also be applied to describing the progression of disease and the action of drugs to change disease progress. Quantitative models for symptomatic and disease-modifying effects of drugs are valuable not only for describing drugs and diseases but also for identifying criteria to distinguish between types of drug actions, with implications for regulatory decisions and long-term patient care.

Population modelling of patient responses in antidepressant studies: a stochastic approach

This paper addresses the problem of modelling longitudinal data describing patients' responses in clinical trials. In particular, a systematic approach relying on a system theoretic paradigm is proposed to deal with contexts where limited physiopathological knowledge is available on disease, drug response, and patients' characteristics. The model relies on the notion of patient's health state which summarizes the patient's condition. In order to cope with the limited number of clinical data usually available, the paper considers a very parsimonious realization where the two state variables are the clinical endpoint and its derivative. Within a population framework, the individual response is modelled as the sum of an individual shift and the average response of subjects belonging to the same study, both described as Markovian processes and identified by empirical Bayes techniques. The proposed approach is validated with experimental data from a Phase II, flexible-dose, depression trial. The dose changes due to the flexible-dose scheme are handled as perturbations on the state. The connection between inter-individual variability and model stability is evaluated showing that the introduction of stable poles helps to describe populations whose range of individual responses does not diverge with time. In this way, good individual fittings and visual predictive checks were obtained for the clinical data. The proposed analysis provides a systematic approach to semi-mechanistic modelling when a precise knowledge of the physiological mechanisms of the disease is incomplete or missing.

Continuous-time Markov modelling of flexible-dose depression trials

The aim of this paper is to provide a systematic methodology for modelling longitudinal data to be used in contexts of limited or even absent knowledge of the physiological mechanism underlying the disease time course. Adopting a system-theoretic paradigm, a population response model is developed where the clinical endpoint is described as a function of the patient's health state. In particular, a continuous-time stochastic approach is proposed where the clinical score and its time-derivative summarize the patient's health state affected by a random term accounting for exogenous unpredictable factors. The proposed approach is validated on experimental data from the placebo and drug arms of a Phase II depression trial. Since some subjects in the trial may undergo changes in their treatment dose due to the flexible dosing scheme, dose escalations are modelled as instantaneous perturbations on the state. In its simplest form--an integrated Wiener process--was able to correctly capture the individual responses in both treatment arms. However, a better description of inter-individual variability was obtained by means of a stable Markovian model. Parameter estimation has been carried out according to the empirical Bayes method.

Population model of longitudinal FEV1 data in asthmatics: meta-analysis and predictability of placebo response

Asthma is an obstructive lung disease where the mechanism of disease progression is not fully understood hence motivating the use of empirical models to describe the evolution of the patient's health state. With reference to placebo response, measured in terms of FEV1 (Forced Expiratory Volume in 1 s), a range of empirical models taken from the literature were compared at a single trial level. In particular, eleven GSK trials lasting 12 weeks in mild-to-moderate asthma were used for the modelling of longitudinal placebo responses. Then, the chosen exponential model was used to carry out an individual participant data meta-analysis on eleven trials. A covariate analysis was also performed to find relevant covariates in asthma to be accounted for in the meta-analysis model. Age, gender, and height were found statistically significant (e.g. the taller the patients the higher the FEV1, the older the patients the lower the FEV1, and females have lower FEV1). By truncating each trial at week 4, the predictive properties of the meta-analysis model were also investigated, showing its ability to predict long-term FEV1 response from truncated trials. Summarizing, the study suggests that: (i) the exponential model effectively describes the placebo response; (ii) the meta-analysis approach may prove helpful to simulate new trials as well as to reduce trial duration in view of its predictive properties; (iii) the inclusion of available covariates within the meta-analysis model provides a reduction of the inter-individual variability.

Use of band-pass filter analysis to evaluate outcomes in an antidepressant trial for treatment resistant patients

Band-pass filtering is a novel statistical methodology that proposes that filtering out data from trial sites generating non-plausible high or low levels of placebo response can yield a more accurate effect size and greater separation of active drug (when efficacious) from placebo. We applied band-pass filters to re-analyze data from a negative antidepressant trial (NCT00739908) evaluating CX157 (a reversible and selective monoamine oxidase inhibitor-A) versus placebo. 360 patients from 29 trial sites were randomized to either CX157 treatment (n=182) or placebo (n=178). We applied two filters of<3 or>7 points (filter #1) or<3 and>9 points (filter #2) mean change of the total MADRS placebo scores for each site. Trial sites that had mean placebo MADRS score changes exceeding the boundaries of these band-pass filter thresholds were considered non-informative and all of the data from these sites were excluded from the post-hoc re-analysis. The two band-pass filters reduced the sample of informative patients from 353 patients in the mITT population to 62 in filter #1 and 152 in the filter #2 group. The placebo response was reduced from 31.1% in the mITT population to 9.4% with filter #1 and 20.8% with filter #2. MMRM analysis revealed a non-statistically significant trend of p=0.13 and 0.16 for the two filters in contrast to the mITT population (p= 0.58). Our findings support the band-pass filter hypothesis and highlight issues related to site-based scoring variability and inappropriate subject selection that may contribute to trial failure.

Evaluation of antidepressant properties of the p38 MAP kinase inhibitor losmapimod (GW856553) in Major Depressive Disorder: Results from two randomised, placebo-controlled, double-blind, multicentre studies using a Bayesian approach

Pro-inflammatory cytokines (PICs) may play important pathophysiological roles in some forms of Major Depressive Disorder (MDD). The p38 MAPK inhibitor losmapimod (GW856553) attenuates the pro-inflammatory response in humans by reducing PIC production. Losmapimod (7.5 mg BD) was administered for 6 weeks in two randomised, placebo-controlled trials in subjects with MDD enriched with symptoms of loss of energy/interest and psychomotor retardation (Studies 574 and 009). Primary efficacy endpoints were the Bech 6-item depression subscale of the HAMD-17 (the 'Bech,') for Study 009; and the Bech, Inventory of Depressive Symptomatology-Clinician Rated (IDS-C), HAMD-17, and Quick Inventory of Depressive Symptomatology (self-rated) (QIDS-SR) for Study 574. Key cytokine biomarker levels were also measured. Study 574 (n=24) was terminated prematurely in light of emerging data from an internal study in rheumatoid arthritis. Efficacy results available at termination favoured losmapimod (Bech, 6 weeks: endpoint drug vs. placebo difference = -4.10; 95% CI, -7.36, -0.83; p=0.017). A subsequent study, Study 009 (n=128), designed using a Bayesian approach based on a prior derived from Study 574, showed no advantage for losmapimod (Bech, 6 weeks: endpoint drug vs. placebo difference = 1.11; 95% credible interval, -0.22, 2.50). Biomarker data showed no significant changes. In conclusion 7.5 mg BID losmapimod was not effective in MDD.

Delayed mood transitions in major depressive disorder

The hypothesis defended here is that the process of mood-normalizing transitions fails in a significant proportion of patients suffering from major depressive disorder. Such a failure is largely unrelated to the psychological content. Evidence for the hypothesis is provided by the highly variable and unpredictable time-courses of the depressive episodes. The main supporting observations are: (1) mood transitions within minutes or days have been reported during deep brain stimulation, naps after sleep deprivation and bipolar mood disorders; (2) sleep deprivation, electroconvulsive treatment and experimental drugs (e.g., ketamine) may facilitate mood transitions in major depressive disorder within hours or a few days; (3) epidemiological and clinical studies show that the time-to-recovery from major depressive disorder can be described with decay models implying very short depressive episodes; (4) lack of relationship between the length of depression and recovery episodes in recurrent depression; (5) mood fluctuations predict later therapeutic success in major depressive disorder. We discuss some recent models aimed to describe random mood transitions. The observations together suggest that the mood transitions have a wide variety of apparently unrelated causes. We suggest that the mechanism of mood transition is compromised in major depressive disorder, which has to be recognized in diagnostic systems.

Amyotrophic lateral sclerosis disease progression model

Our objective was to develop: 1) a longitudinal model to describe amyotrophic lateral sclerosis (ALS) disease progression using the revised Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R); and 2) a probabilistic model to estimate the presence of clusters of trajectories in ALS progression over 12 months of treatment. Three hundred and thirty-eight patients treated with placebo from the PRO-ACT database were included in the analyses. A non-linear Weibull model best described the ALS disease progression, and a stepwise logistic regression approach was used to select the variables predicting a slow or fast disease progression. Results identified two clusters of trajectories: 1) slow disease progressors (46% of patients with a change from baseline of 13%); 2) fast disease progressors (54% of patients with a change from baseline of 49%). ROC curve analysis estimated the optimal cut-off for classifying patients as slow or fast disease progressors given ALSFRS-R measurements at 2-4 weeks. Results showed that the degree of ALS disease progression quantified by the ALSFRS-R symptomatic change on placebo is highly heterogeneous. In conclusion, this finding indicates the potential interest of disease progression models for implementing a population enrichment strategy to control the level of heterogeneity in the patients included in new trials.

Bayesian model of Hamilton Depression Rating Score (HDRS) with memantine augmentation in bipolar depression

AIM

Presynaptic and post-synaptic glutamatergic modulation is associated with antidepressant activity that takes several weeks to reach a maximal full effect. Limiting mood elevating effects after single drug administration may be the result of compensatory synaptic processes. Therefore, using augmentation treatment with agents having presynaptic and post-synaptic effects on the glutamatergic system, this study aims to evaluate the effect of augmentation therapy on the rate of change in mood elevation in patients with bipolar depression.

METHODS

In a pilot study, 29 outpatients with bipolar depression on a stable lamotrigine dose regimen received placebo or memantine pills daily (titrated up by 5 mg week⁻¹ to 20 mg) in a randomized, double-blind, parallel group, 8 week study. Patients were evaluated weekly using the 17-item Hamilton Depression Rating Score (HDRS) and all data were analyzed simultaneously. Linear, exponential, maximal effect, Gompertz and inverse Bateman functions were evaluated using a Bayesian approach population pharmacodynamic model framework. In these models, differences in parameters were examined across the memantine and placebo augmentation groups.

RESULTS

A Gompertz function with a treatment switch on the parameter describing the speed of HDRS decline (γ, 95% confidence interval [CI]) best described the data (γ(memantine) = 1.8, 95% CI 0.9, 3.6), γ(placebo) = 1.2, 95% CI 0.5, 3.5)). Between subject variability was identified on baseline HDRS (2.9, 95% CI 1.5, 4.4) and amplitude of score improvement (4.3, 95% CI 2.7, 6.5).

CONCLUSIONS

This pharmacodynamic approach identified an increased speed of response after memantine augmentation, compared with placebo augmentation in bipolar depression patients.

A PCA approach to population analysis: with application to a Phase II depression trial

For psychiatric diseases, established mechanistic models are lacking and alternative empirical mathematical structures are usually explored by a trial-and-error procedure. To address this problem, one of the most promising approaches is an automated model-free technique that extracts the model structure directly from the statistical properties of the data. In this paper, a linear-in-parameter modelling approach is developed based on principal component analysis (PCA). The model complexity, i.e. the number of components entering the PCA-based model, is selected by either cross-validation or Mallows' Cp criterion. This new approach has been validated on both simulated and clinical data taken from a Phase II depression trial. Simulated datasets are generated through three parametric models: Weibull, Inverse Bateman and Weibull-and-Linear. In particular, concerning simulated datasets, it is found that the PCA approach compares very favourably with some of the popular parametric models used for analyzing data collected during psychiatric trials. Furthermore, the proposed method performs well on the experimental data. This approach can be useful whenever a mechanistic modelling procedure cannot be pursued. Moreover, it could support subsequent semi-mechanistic model building.

Assessing the sources of unreliability (rater, subject, time-point) in a failed clinical trial using items of the Positive and Negative Syndrome Scale (PANSS)

BACKGROUND

Considering the increasing attention to the study of failed clinical trials, the goal of this study was to identify the sources of unreliability in a failed clinical trial by assessing scores on the Positive and Negative Syndrome Scale (PANSS).

METHODS

This study is a substudy from a failed phase 2 double-blind, placebo-controlled trial of schizophrenia. Using the generalizability theory, this substudy assesses reliability on 3 conditions: raters, time points (PANSS evaluations, 1 week apart), subjects for 3 groups (placebo responders, placebo nonresponders, and treatment group).

RESULTS

The placebo response rate was 40.07% (32/71). For all PANSS positive symptom items, the most variability was for raters (range, 33%-72%) for the placebo responders, 31% to 68% for the placebo nonresponders, and 29% to 60% for the treatment group. The variability of the interaction of rater and time point was the second source of unreliability, with an average of 12.28% compared to 12.00% for the placebo nonresponders and 10.00% for the treatment group. All items of the negative symptom subscale showed the most percent variability for raters, for all groups. For general psychopathology items (except preoccupation), raters accounted for the most variability in the scores for placebo responders with an average of 51.00% across items. A similar pattern was observed for the placebo nonresponders and for the treatment group; for the treatment group, the interaction between rater and time point accounted for the most variability for somatic concern and anxiety.

CONCLUSIONS

Results confirm the efficacy of applying the generalizability theory to the estimation of reliability to identify a source of unreliability and provide evidence for the relationship between low reliability and failed trials. Findings can be used to guide data monitoring, rater training, and identification of PANSS items, which may require supplementary training.

Exposure-response modeling of anti-depressant treatments: the confounding role of placebo effect

Emerging evidence indicates that the selection of patients and the selection of recruitment centers in placebo-controlled, randomized clinical trials (RCTs) in depression have a substantial impact on the probability of observing a treatment effect of a novel medication. The objective of this work was to evaluate the role of placebo in characterizing the exposure-response relationship and proposes a new methodology based on band-pass filtering for assessing the relationship between drug exposure, level of clinical response conditioned to the level of placebo response, and the potency of the antidepressant drug evaluated. Clinical trial simulation (CTS) was used to demonstrate that the conventional analyses of data generated in multi-centre RCTs including centres exhibiting heterogeneous placebo response can lead to contradictory and inappropriate assessment of the exposure-response relationship. To address this issue, a novel modelling approach has been developed for establishing the exposure-response relationship by using a pharmacodynamic model accounting for the placebo effect. The proposed model demonstrated that the expected drug related treatment effect in a placebo-controlled RCT can be predicted as a function of the dose, the drug potency and the level of placebo response when data from informative recruitment centres are considered. With this novel approach, a more accurate estimate of the dose/exposure response can be derived and more informed go/no-go decisions can be made in developing drugs for psychiatric disorders.

Increasing trial efficiency by early reallocation of placebo nonresponders in sequential parallel comparison designs: Application to antidepressant trials

Background

The sequential parallel comparison (SPC) design was proposed to improve the efficiency of psychiatric clinical trials by reducing the impact of placebo response. It consists of two consecutive placebo-controlled comparisons of which the second is only entered by placebo nonresponders from the first. Previous studies suggest that in antidepressant trials, nonresponse to placebo can already be predicted after 2 weeks of follow-up. This would allow to reduce the first phase of the SPC design to further increase its efficiency.

Purpose

To compare the sample size requirements of an 8-week randomized controlled trial (RCT8) and alternative SPC designs with equal or longer total follow-up duration (SPC2+6, SPC4+4, and SPC6+6).

Methods

Scenarios for response and dropout rates were defined. Sample sizes to achieve 80% power were determined for the various designs. Three treatment functions assumed either a smaller, equal, or larger effect at the early stage of the trial as compared with that at the end. Two dropout models described either predominantly early or linearly increasing dropout, and dropout was considered as nonresponse. The relative efficiency of the different designs was evaluated across these scenarios and for a specific scenario based on empirical antidepressant trial data.

Results

The different SPC designs (i.e., SPC2+6, SPC4+4, and SPC6+6) were generally more efficient than the RCT8 design when the treatment effect at the early stage of the trial was equal or larger than the effect at the end. In this case, the advantage of the SPC designs increased in the presence of dropout. The SPC2+6 design was usually more efficient than the SPC4+4 design and was relatively less affected by dropout when it occurred predominantly early. For the scenario that was based on antidepressant trial data, the SPC2+6 and SPC4+4 designs required 51% and 53% fewer patients than the RCT8 design.

Limitations

A limited variety of scenarios was evaluated. Parameter values resembled those observed in antidepressant trials.

Conclusions

This study suggests that SPC designs are highly efficient alternatives to a conventional RCT in indications where placebo response is high and substantial treatment effects are established after a relatively short follow-up period (i.e., after the first SPC design phase). We conclude that SPC designs can reduce sample size requirements and increase success rates of antidepressant trials.

Joint modeling of efficacy, dropout, and tolerability in flexible-dose trials: a case study in depression

Many difficulties may arise during the modeling of the time course of Hamilton Rating Scale for Depression (HAM D)scores in clinical trials for the evaluation of antidepressant drugs: (i) flexible designs, used to increase the chance of selecting more efficacious doses, (ii) dropout events, and (iii) adverse effects related to the experimental compound.It is crucial to take into account all these factors when designing an appropriate model of the HAM D time course and to obtain a realistic description of the dropout process. In this work, we propose an integrated approach to the modeling of a double-blind, flexible-dose, placebo-controlled, phase II depression trial that comprises response,tolerability, and dropout. We investigate three different dropout mechanisms in terms of informativeness. Goodness of fit is quantitatively assessed with respect to response (HAM D score) and dropout data. We show that dropout is a complex phenomenon that may be influenced by HAM D evolution, dose changes, and occurrence of drug-related adverse effects.

Trial Simulation to estimate Type I error when a population window enrichment strategy is used to improve efficiency of clinical trials in depression

Performance of recruitment centers is a critical feature for the success of multicentric randomized clinical trials (RCT). An enrichment window strategy (EWS) was recently proposed to enhance signal detection in RCTs based on the identification of centers with non-plausible placebo response, leading to a Per Protocol exclusion of all data from these non-informative centers before statistical analysis. The risk of an inflated Type I error associated with EWS was assessed using Clinical Trial Simulation. Randomized two-arm placebo controlled trials were simulated under the assumption that the drug treatment was ineffective.The results obtained in the absence of population enrichment were compared to the results obtained after EWS application. The results indicated that EWS preserved the Type I error showing no bias, at variance with other procedures. These results were confirmed by simulating 13 three-arm placebo controlled RCTs from the GSK Clinical Database.

Trajectories of depression severity in clinical trials of duloxetine: insights into antidepressant and placebo responses

CONTEXT

The high percentage of failed clinical trials in depression may be due to high placebo response rates and the failure of standard statistical approaches to capture heterogeneity in treatment response.

OBJECTIVE

To assess whether growth mixture modeling can provide insights into antidepressant and placebo responses in clinical trials of patients with major depression.

DESIGN

We reanalyzed clinical trials of duloxetine to identify distinct trajectories of Hamilton Scale for Depression (HAM-D) scores during treatment. We analyzed the trajectories in the entire sample and then separately in all active arms and in all placebo arms. Effects of duloxetine hydrochloride, selective serotonin reuptake inhibitor (SSRI), and covariates on the probability of following a particular trajectory were assessed. Outcomes in different trajectories were compared using mixed-effects models.

SETTING

Seven randomized double-blind clinical trials of duloxetine vs placebo and comparator SSRI. Patients A total of 2515 patients with major depression.

INTERVENTIONS

Duloxetine and comparator SSRI. Main Outcome Measure Total score on the HAM-D.

RESULTS

In the entire sample and in the antidepressant-treated subsample, we identified trajectories of responders (76.3% of the sample) and nonresponders (23.7% of the sample). However, placebo-treated patients were characterized by a single response trajectory. Duloxetine and SSRI did not differ in efficacy, and compared with placebo they significantly decreased the odds of following the nonresponder trajectory. Antidepressant responders had significantly better HAM-D scores over time than placebo-treated patients, but antidepressant nonresponders had significantly worse HAM-D scores over time than the placebo-treated patients.

CONCLUSIONS

Most patients treated with serotonergic antidepressants showed a clinical trajectory over time that is superior to that of placebo-treated patients. However, some patients receiving these medications did more poorly than patients receiving placebo. These data highlight the importance of ongoing monitoring of medication risks and benefits during serotonergic antidepressant treatment. They should further stimulate the search for biomarkers or other predictors of responder status in guiding antidepressant treatment.

General and comparative efficacy and effectiveness of antidepressants in the acute treatment of depressive disorders: a report by the WPA section of pharmacopsychiatry

Current gold standard approaches to the treatment of depression include pharmacotherapeutic and psychotherapeutic interventions with social support. Due to current controversies concerning the efficacy of antidepressants in randomized controlled trials, the generalizability of study findings to wider clinical practice and the increasing importance of socioeconomic considerations, it seems timely to address the uncertainty of concerned patients and relatives, and their treating psychiatrists and general practitioners. We therefore discuss both the efficacy and clinical effectiveness of antidepressants in the treatment of depressive disorders. We explain and clarify useful measures for assessing clinically meaningful antidepressant treatment effects and the types of studies that are useful for addressing uncertainties. This includes considerations of methodological issues in randomized controlled studies, meta-analyses, and effectiveness studies. Furthermore, we summarize the differential efficacy and effectiveness of antidepressants with distinct pharmacodynamic properties, and differences between studies using antidepressants and/or psychotherapy. We also address the differential effectiveness of antidepressant drugs with differing modes of action and in varying subtypes of depressive disorder. After highlighting the clinical usefulness of treatment algorithms and the divergent biological, psychological, and clinical efforts to predict the effectiveness of antidepressant treatments, we conclude that the spectrum of different antidepressant treatments has broadened over the last few decades. The efficacy and clinical effectiveness of antidepressants is statistically significant, clinically relevant, and proven repeatedly. Further optimization of treatment can be helped by clearly structured treatment algorithms and the implementation of psychotherapeutic interventions. Modern individualized antidepressant treatment is in most cases a well-tolerated and efficacious approach to minimize the negative impact of otherwise potentially devastating and life-threatening outcomes in depressive disorders.

Structural models describing placebo treatment effects in schizophrenia and other neuropsychiatric disorders

Large variation in placebo response within and among clinical trials can substantially affect conclusions about the efficacy of new medications in psychiatry. Developing a robust placebo model to describe the placebo response is important to facilitate quantification of drug effects, and eventually to guide the design of clinical trials for psychiatric treatment via a model-based simulation approach. In addition, high dropout rates are very common in the placebo arm of psychiatric clinical trials. While developing models to evaluate the effect of placebo response, the data from patients who drop out of the trial should be considered for accurate interpretation of the results. The objective of this paper is to review the various empirical and semi-mechanistic models that have been used to quantify the placebo response in schizophrenia trials. Pros and cons of each placebo model are discussed. Additionally, placebo models used in other neuropsychiatric disorders like depression, Alzheimer's disease and Parkinson's disease are also reviewed with the objective of finding those placebo models that could be useful for clinical studies of both acute and chronic schizophrenic disease conditions. Better understanding of the patterns of dropout and the factors leading to dropouts are crucial in identifying the true placebo response. We therefore also review dropout models that are used in the development of models for treatment effects and in the optimization of clinical trials by simulation approaches. The use of an appropriate modelling strategy that is capable of identifying the potential sources of variable placebo responses and dropout rates is recommended for improving the sensitivity in discriminating between the effects of active treatment and placebo.

Novel study designs to investigate the placebo response

Background

Investigating the size and mechanisms of the placebo response in clinical trials have relied on experimental procedures that simulate the double-blind randomized placebo-controlled design. However, as the conventional design is thought to elucidate drug rather than placebo actions, different methodological procedures are needed for the placebo response.

Methods

We reviewed the respective literature for trials designs that may be used to elucidate the size of the placebo response and the mechanisms associated with it.

Results

In general, this can be done by either manipulation the information provided to the subjects, or by manipulation the timing of the drug applied. Two examples of each strategy are discussed: the "balanced placebo design" (BDP) and the "balanced cross-over design" (BCD) and their variants are based on false information, while the "hidden treatment" (HT) and the ""delayed response test" (DRT) are based on manipulating the time of drug action. Since most such approaches include deception or incomplete information of the subjects they are suitable for patient only with authorized deception.

Conclusion

Both manipulating the information provided to subjects (BDP, DCD) or manipulating the timing of drug application (HT, DRT) allows overcoming some of the restrictions of conventional drug trials in the assessment of the placebo response, but they are feasible mostly in healthy subjects for ethical reasons.

From trial and error to trial simulation III: a framework for interim analysis in efficacy trials with antidepressant drugs

Clinical trials with antidepressant drugs often fail to detect drug effect, even with drugs that are known to be efficacious. In a previous publication, we showed that a model-based approach is required to address some of the existing challenges in the design of clinical trial protocols. Here, we illustrate how the implementation of an interim analysis (IA) may help to identify studies that are headed for failure, early in the trial before completion of treatment. In contrast to traditional IA procedures, an adaptive Bayesian approach is proposed to optimize the timing of analysis and decision criteria for futility and efficacy, taking into account enrollment rate and treatment response at intermediate visits in the trial. Validation procedures involving re-enrollment of patients confirmed the performance of the method. Our findings reveal that optimization of the timing and decision criteria at the interim stage is critical for the accuracy of the conclusions about treatment efficacy or futility.

A new population-enrichment strategy to improve efficiency of placebo-controlled clinical trials of antidepressant drugs

The rate-limiting factor in the discovery of novel antidepressants is the inefficient methodology of traditional multicenter randomized clinical trials (RCTs). We applied a model-based approach to a large clinical database (five RCTs in major depressive disorder (MDD), involving 1,837 patients from 124 recruitment centers) with two objectives: (i) to learn about the role of center-specific placebo response in RCT failure and (ii) to apply what is learned to improve the efficiency of RCTs by enhancing the detection of treatment effect (TE). Sensitivity analysis indicated that center-specific placebo response was the most relevant predictor of RCT failure. To reduce the statistical "noise" generated by centers with nonplausible, excessively high/low placebo responses, we developed an enrichment-window strategy. Clinical trial simulation was used to assess the enrichment strategy applied before the standard statistical analysis, resulting in an overall reduction in failure of RCTs from ~50 to ~10%.

Bayesian quantitative electrophysiology and its multiple applications in bioengineering

Bayesian interpretation of observations began in the early 1700s, and scientific electrophysiology began in the late 1700s. For two centuries these two fields developed mostly separately. In part that was because quantitative Bayesian interpretation, in principle a powerful method of relating measurements to their underlying sources, often required too many steps to be feasible with hand calculation in real applications. As computer power became widespread in the later 1900s, Bayesian models and interpretation moved rapidly but unevenly from the domain of mathematical statistics into applications. Use of Bayesian models now is growing rapidly in electrophysiology. Bayesian models are well suited to the electrophysiological environment, allowing a direct and natural way to express what is known (and unknown) and to evaluate which one of many alternatives is most likely the source of the observations, and the closely related receiver operating characteristic (ROC) curve is a powerful tool in making decisions. Yet, in general, many people would ask what such models are for, in electrophysiology, and what particular advantages such models provide. So to examine this question in particular, this review identifies a number of electrophysiological papers in bioengineering arising from questions in several organ systems to see where Bayesian electrophysiological models or ROC curves were important to the results that were achieved.

From trial and error to trial simulation. Part 1: the importance of model-based drug development for antidepressant drugs

Clinical trial simulation (CTS) allows the investigation of the influence of design characteristics on important aspects of clinical trials such as power and type I error. Simulation scenarios may be critical to decision making and prevention of study failure. The analysis and simulation of clinical trials in depression have, however, suffered from a lack of disease/dropout models that describe the individual time course of the clinical scale of interest. We propose a new model with dual random effects (DREM) derived from functional data analysis, which provides unbiased estimates of parameters and is suitable for the purposes of clinical trial simulation. A comparison of model performance is presented, along with standard statistical methods using various goodness-of-fit criteria. Our results show that data simulated using the DREM closely match individual patient data, including real-life dropout scenarios. In addition, parameterization in terms of interindividual variability ensures easier explanation of findings to clinical scientists, who ultimately make the relevant decisions.

Does early improvement triggered by antidepressants predict response/remission? Analysis of data from a naturalistic study on a large sample of inpatients with major depression

BACKGROUND

Delayed onset of efficacy of antidepressants and a high proportion of depressed patients being poor or non-responders to antidepressants are well known clinical challenges. Therefore, it seems to be necessary to identify predictors for response and - even more important - for remission. It has been suggested that reduction of depressive symptoms at an early stage of antidepressant treatment may predict treatment outcome. Our objective was to test, if this hypothesis derived from randomized controlled studies (RCTs) in outpatients, would be confirmed in a large naturalistic study in a cohort of inpatients with major depression. Patients were treated with various antidepressants and co-medication according to the protocol based on evidence-based clinical guidelines.

METHODS

This was a large naturalistic prospective study. All patients (N=795) were hospitalized and met DSM-IV criteria for major depression according to a structured clinical interview (SCID). Assessments were conducted biweekly. Several definitions of early improvement (20%, 25% and 30% reduction in HAMD-21 baseline total scores) at two different visits were tested. Sensitivity, specificity and predictive values were calculated for the different definitions of early improvement. ROC-analyses as well as logistic regression models have been performed. Response was defined as 50% improvement of the total baseline HAMD-21 score and remission as a score of </=7 at discharge. Additionally, time to response was analyzed by computing Kaplan-Meier survival estimates for the "best" early improvement definition in comparison to non early improvement. Subgroup analyses were conducted to test whether the results were consistent across treatment subgroups.

RESULTS

48.8% of patients in our sample were remitters. The overall response rate was 79.6%. A 20% reduction of HAMD-21 total baseline score at Day 14 provided a sensitivity of 75% and a specificity of 59% for response prediction. This definition of early improvement was an even more sensitive predictor for remission (80%) with a limited specificity (43%). The AUC value of about 0.68 for early response (20% improvement) indicates good predictability for both time intervals tested (Day 14 and Day 28) and changed only marginally with increased percentages in score reduction (AUC=0.71 and 0.73, respectively). More than one third (37%) of all patients who had not improved at Day 14 showed not response in the later treatment course (this was the case for nearly half of all patients (43%) at Day 28). Similar results were obtained by Kaplan-Meier survival analyses. Log-rank test showed significantly longer time to response in patients with non-early improvement (p<0.0001).

LIMITATIONS

Results were assessed by a post-hoc analysis based on prospectively collected data. Several caveats of a naturalistic design must be mentioned, especially there was no control group and only a limited number of stratification factors could be considered.

CONCLUSION

The results support earlier findings that early improvement in the first two weeks may predict with high sensitivity later response and remission, even in hospitalized patients suffering from a more severe degree of depression. Since we used a naturalistic study design, the data may be considered as a replication of previous results drawn from RCTs in a naturalistic environment. We found a global antidepressant effect which was consistent across treatment subgroups regarding sensitivity values. However, we are aware of the inability of effectiveness studies to draw causal treatment relationships from the uncontrolled approach. Nevertheless, the replication of previous results might indicate that a drug switch during treatment in case of lack of early improvement could be accelerated.

Quantitative disease, drug, and trial models

Quantitative disease-drug-trial models allow learning from prior experience and summarize the knowledge in a ready to apply format. Employing these models to plan future development is proposed as a powerful solution to improve pharmaceutical R&D productivity. The disease and trial models are, to a large extent, independent of the product, but the drug model is not. The goals are to apply the disease and trial models to future development and regulatory decisions, and publicly share them. We propose working definitions of these models, describe the various subcomponents, provide examples, and discuss the challenges and potential solutions for developing such models. Building useful disease-drug-trial models is a challenging task and cannot be achieved by any single organization. It requires a consorted effort by industry, academic, and regulatory scientists. We also describe the strategic goals of the FDA Pharmacometrics group.

Modelling placebo response in depression trials using a longitudinal model with informative dropout

Dropouts are common events in longitudinal studies in depression. Ignoring missing information may lead to biased and inconsistent assessment of study results. A non-linear model was recently developed to describe the time-course of HAMD-17 clinical score in the placebo arms of antidepressant clinical trials. In this paper we complemented this model by introducing an informative dropout component to jointly estimate HAMD-17 time-course and dropout mechanism. The aims of this work were to: (a) characterise typical placebo response in depression trials in presence of dropouts, (b) explore which dropout mechanism better describe the time-varying probability of a subject to dropout from the trial, and (c) define a framework for the development of clinical trial simulation in depression. A meta-analytic approach was used on placebo data collected in 6 clinical trials including 695 subjects suffering from Major Depressive Disorders. Alternative hypotheses for "missingness" were evaluated using different hazard models. The "Missing Not At Random" performed statistically (p<0.01) better than "Missing At Random", that in turn performed better (p<0.01) than "Missing Completely At Random" model. This finding provided new insights on the validity of the analyses currently used in many longitudinal clinical trials to support the registration of a new medicinal product.

New insights into the placebo and nocebo responses

In modern medicine, the placebo response or placebo effect has often been regarded as a nuisance in basic research and particularly in clinical research. The latest scientific evidence has demonstrated, however, that the placebo effect and the nocebo effect, the negative effects of placebo, stem from highly active processes in the brain that are mediated by psychological mechanisms such as expectation and conditioning. These processes have been described in some detail for many diseases and treatments, and we now know that they can represent both strength and vulnerability in the course of a disease as well as in the response to a therapy. However, recent research and current knowledge raise several issues that we shall address in this review. We will discuss current neurobiological models like expectation-induced activation of the brain reward circuitry, Pavlovian conditioning, and anxiety mechanisms of the nocebo response. We will further explore the nature of the placebo responses in clinical trials and address major questions for future research such as the relationship between expectations and conditioning in placebo effects, the existence of a consistent brain network for all placebo effects, the role of gender in placebo effects, and the impact of getting drug-like effects without drugs.